Growth in fluctuating light buffers plants against photorespiratory perturbations

Photorespiration (PR) is the pathway that detoxifies the product of the oxygenation reaction of Rubisco. It has been hypothesized that in dynamic light environments, PR provides a photoprotective function. To test this hypothesis, we characterized plants with varying PR enzyme activities under fluctuating and non-fluctuating light conditions. Contrasting our expectations, growth of mutants with decreased PR enzyme levels was least affected in fluctuating light compared with wild type. Results for growth, photosynthesis and metabolites combined with thermodynamics-based flux analysis revealed two main causal factors for this unanticipated finding: reduced rates of photosynthesis in fluctuating light and complex re-routing of metabolic fluxes. Only in non-fluctuating light, mutants lacking the glutamate:glyoxylate aminotransferase 1 re-routed glycolate processing to the chloroplast, resulting in photooxidative damage through H2O2 production. Our results reveal that dynamic light environments buffer plant growth and metabolism against photorespiratory perturbations.

c. Fv/Fm was calculated separately from old and young leaves, defined as leaves with and without visible petiole, respectively.Genotypes separated into three groups: WT and ggt2 increased Fv/Fm slightly after the light shift and young leaves had significantly lower Fv/Fm as compared to old leaves (upper graph); gldt1-1 and hpr1 lines decreased Fv/Fm upon a shift to CL, with a more pronounced effect in old compared with young leaves (middle graph).ggt1 lines decreased Fv/Fm upon a shift to CL without discernable differences between young and old leaves (bottom graph).Averages of n = 10 for all genotypes except for n = 7 for hpr1-1 (t4), n = 8 for hpr1-2 (t4), n = 9 for hpr1-1 (t0-t3), gldt1-1 (t0-t4) and ggt1-2 (t4) and n = 11 for hpr1-2 (t0-t3) ± standard deviation are shown.Asterisks in the corresponding color indicate significant differences between young and old leaves at a given time point determined via two-way ANOVA and post-hoc Tukey multiple comparison test with p < 0.05.To exclude background signal due to algal growth on the soil, the outline of each plant was selected manually to generate the average Fv/Fm.3 and Supplementary Fig. 9. Membranes were stained with Ponceau Red (a-b) and subsequently incubated with anti-GGT1/2 (c-d), anti-HPR1 (e-f) and anti-ascorbate peroxidase (APX; g-j).Anti-GGT1/2 binds both GGT1 and GGT2.Anti-APX recognizes all four isoforms of APX, with peroxisomal and cytosolic APX being indistinguishable from one another on this blot.The three bands in each blot from the top to bottom correspond to thylakoid (t), stromal (s), and both peroxisomal and cytosolic (p+c) APX.When tAPX and sAPX bands came up weak during a short exposure time (g-h), the same membrane was exposed for a longer time (i-j).The quantitative analysis of immunoblot signals from both light shifts is summarized in Supplementary Data 2-3.

Detailed workflow to model flux distributions in photorespiratory mutants
The flux distributions were predicted following the workflow outlined below: 1. Optimize   for wild type model (under control light) The TMFA problem is solved to obtain a thermodynamically feasible flux distribution that yields the highest predicted relative growth rate.The optimization problem contains additional constraints on the ratio between the Rubisco oxygenation and carboxylation reactions (fixed within one standard deviation,    = 0.087,    = 0.059) and photon uptake (Supplementary Data 8).The upper limit for the photon uptake was determined by scaling the reaction upper bound of 1000 mmol gDW -1 h -1 by the ratio of the experimental light intensities (CL: 200 µmol photons m -2 s -1 , FL: 90 µmol photons m -2 s -1 ) by a saturating light intensity of 700 µmol photons m -2 s -1 .max   s.t.
The constraints in Eq. ( 8) define the lower and upper limits for the ratio between the Rubisco oxygenation and carboxylation reaction, respectively.The values for  and   were specific for the control light condition.A tolerance of 10% was added to ensure feasibility of this and the following optimization problems.

Set concentration limits for measured metabolites
Measured metabolite concentrations as converted from metabolite abundances used to limit metabolite concentrations in the TMFA program within one standard deviation: =  −     =  +   .

Check if program is feasible
Yes → proceed to step 5 No → relax constraints on metabolite concentrations by solving the following program: Eq. ( 1)-( 7) (TMFA constraints) The program above introduces relaxations () to the concentration limits of measured metabolites and maximizes the weighted sum of relative growth rate and the sum of relaxations.
The constraint   ≥ (1 − 0.001) ⋅    imposes a lower bound on the flux through the biomass reaction which is 99.9% of the optimal value determined in step 1.The tolerance is introduced here and in the following steps (where indicated) to ensure feasibility of the respective optimization problem.
Once  has been determined, the metabolite concentrations are re-calculated and the lower and upper limits for metabolite concentrations updated as follows: ′ = 1.1 ⋅ (   +   ), ∀ ∈ ().

Solve TMFA problem, from step 1, above with (relaxed) measured metabolite concentrations
After solving the problem,    is updated.

Minimize the sum of fluxes in the wild type model
Eq. ( 1)-( 7) (TMFA constraints) The optimal value, i.e. minimum distance is denoted   .

Determine feasible flux ranges using TVA
For each reaction  the following problems are solved: Eq. ( 1)-( 7) (TMFA constraints) The constraint  ≤ (1 + 0.003) ⋅   imposes an upper limit on the value of , which is 100.3% of the previously determined optimum   (step 6) to ensure feasibility of the TVA programs.

Sample random flux distributions that satisfy the constraints from step 7
Generate a vector  * containing random fluxes for each reaction within the feasible ranges determined in step 7. Subject to all constraints from step 7, a minimization problem is solved to find a flux distribution that minimizes the distance to  * (min| * − |).This problem is solved with 1000 different  * samples to obtain 1000 feasible flux distributions.The optimal growth rate determined for the wild type in step 5 (   ) is referred to as    .The relative growth rate of the mutant is fixed between a lower and an upper bound, which are calculated as follows: The lower and upper bounds on net CO2 assimilation rate are given by: Fluxes with superscript "wt" originate from the flux distribution predicted in step 5, while values for  were determined experimentally (Supplementary Data 8).

d. Add constraints on oxygenation to carboxylation ratio and photon uptake
oxygenation to carboxylation ratio: The determined value for   is then used to update the lower and upper bounds on the predicted relative growth rate of the mutant: Eq. ( 1)-( 7 See step 9 for objective and explanation.

Create TMFA problem for FL-specific wild type model
The values for  and  ℎ  for the Col-0 wild type in FL were used as constraints as in step 1.

Fix 𝒗 𝒃𝒊𝒐 according to ratio of RGR to Col-0 wild type in CL
The optimal growth rate determined for the wild type in CL (step 5,    ) is referred to as    .The relative growth rate of the wild type in FL is fixed between a lower and an upper bound, which are calculated by:   , = (1 − 10 −3 ) ⋅    ⋅  ,  , and   , = (1 + 10 −3 ) ⋅    ⋅  ,  , .

Fix net CO2 assimilation rate according to ratio of 𝑨 to Col-0 wild type in CL
Similar to the relative growth rate, the ratio on  is fixed using the measured values for the wild type Fluxes with superscript "CL" originate from the flux distribution predicted in step 5, while values for  were determined experimentally (Supplementary Data 8).

Check if program is feasible
Yes → Proceed to step 15 No → relax constraint on relative growth rate The following optimization program finds a flux distribution that yields the relative growth rate that is closest to the expected relative growth rate    : =   ,  ,  , .
The upper bound on the photon uptake reaction is alleviated to allow for an increase in predicted relative growth rate: The obtained value for   is called    .
Next, the flux through  ℎ is minimized subject to the constraints from the program shown above, while keeping   at    (± 0.1%).The updated value for  ℎ is used for all subsequent simulations for FL.
15. Repeat steps 3-10 for the FL condition with constraints on   and

Figure 9 .
Fv/Fm of photorespiratory mutants during a shift from control to fluctuating light.a-b.Representative false color images (a) and average (b) maximum quantum yield of PSII (Fv/Fm) of WT, ggt2, two mutant alleles of each ggt1 and hpr1 and gldt1-1 grown under control light (CL: 200 µmol photons m -2 s -1 ; time point t0) and 3 h (t1), 27 h (t2), 75 h (t3) and 123 h (t4) after the shift to fluctuating light (FL: 1 min 700 µmol photons m -2 s -1 , 4 min 70 µmol photons m -2 s -1 ).Per genotype and time point, 10 images with similar results were taken.b, Averages of n = 10 ± standard deviation are shown.Asterisks in the corresponding color indicate significant differences between mutants and WT within one time point and different lowercase letters in the corresponding color significant differences between time points within one genotype determined via two-way ANOVA and post-hoc Tukey multiple comparison test with p < 0.05.To exclude background signal due to algal growth on the soil, the outline of each plant was selected manually to generate the average Fv/Fm.Supplementary Figure 10.Protein levels of GGT1/2, HPR1 and APX isoforms during light shifts.a-j.Immunoblot analysis of total leaf protein extracts from WT, ggt2, ggt1-1, ggt1-2, hpr1-1 and hpr1-2 grown under fluctuating light (FL: 1 min 700 µmol photons m -2 s -1 , 4 min 70 µmol photons m -2 s -1 ; 35 d) or control light (CL: 200 µmol photons m -2 s -1 ; 29 d) and shifted to CL (a, c, e, g, i) or FL (b, d, f, h, j), respectively.Samples were taken before the light shift (36 d in FL or 19 d in CL) and 27 h (1 d) and 123 h (5 d) after the light shift as in Fig.

Figure 12 .
Changes in metabolite levels in response to a shift from control to fluctuating light.a-d.WT, ggt2 and two mutant alleles of each ggt1 and hpr1 were grown under control light (CL: 200 µmol photons m -2 s -1 ) for 29 d and then shifted to fluctuating light (FL: 1 min 700 µmol photons m -2 s -1 , 4 min 70 µmol photons m -2 s -1 ) on day 30.Samples for metabolite extraction were taken 6 h into the light period at 21 h (t0) before and 3 h (t1), 27 h (t2), 75 h (t3) and 123 h (t4) after the light shift.Heatmaps of log2 transformed metabolite averages (n = 3-4) normalized on WT in CL are shown for photorespiratory metabolites (a), carbohydrates (b), amino acids (c) and TCA cycle metabolites (d).Averages of n = 3-4 are shown.Exact number of replicates per group can be found in Supplementary Data 4. Asterisks indicate significant differences between all analyzed mutant alleles and WT at the given time point.Downwards pointing arrows in the corresponding color (WT: black; ggt2: dark blue; both ggt1 lines: light blue; both hpr1 lines: red) indicate significant differences to t0 of the same genotype for at least two subsequent time points in FL as determined via two-way ANOVA and post-hoc Tukey multiple comparison tests with p < 0.05.The complete data set can be found in Supplementary Data 4. Ala -Alanine, Asn -Asparagine, Asp -Aspartic acid, GABA -Υ-aminobutyric acid, Glu -Glutamic acid, Gln -Glutamine, Ile -Isoleucine, Leu -Leucine, Lys -Lysine, Met -Methionine, Phe -Phenylalanine, Pro -Proline, Trp -Tryptophan, Val -Valine, 2-OG -2-oxoglutarate, TP -Time point.
according to ratio to the relative growth rate of Col-0 wild type net CO2 assimilation rate according to ratio of  to Col-0 wild type Similar to the relative growth rate, the ratio on  is fixed using the measured values for the wild type and the respective mutant.The net CO2 assimilation rate predicted by the model is approximated by   − 0.5  −   2 (→) , where   and   are the fluxes through the Rubisco carboxylation and oxygenation reactions, and   2 (→)  export flux of CO2 from the mitochondrion to the cytosol, which approximates dark respiration 4 .
the distance to the wild type flux distributionThe following optimization problem finds the flux distribution with the smallest distance to the wild type flux distribution obtained in step 6: in CL and FL (see step 10c).The lower and upper bounds on net CO2 assimilation rate are given by: ≤   − 0.5  −   2 (→) ≤ ,

Set the upper bound for the distance to the wild type flux distribution, obtained in step 10j l. Determine feasible flux ranges using TVA
The minimum distance obtained from the solution to the problem above is called   . k.